Neurofuzzy Modelling as Part of an Intelligent Oncology Workstation in Breast Cancer Treatment

Download

Full text not available from this repository.

Description/Abstract

In this paper an outline is presented of a neurofuzzy modelling approach as part of an Multimedia Intelligent Oncology Workstation (MINOW) for the improved treatment and diagnosis of breast cancer. The core component of the system is a high-dimensional approximator neurofuzzy network, ASMOD, introduced by Kavli and implemented by researchers at the University of Southampton. This neurofuzzy constructive learning algorithm may be used to automatically generate high-dimensional approximations to identify complex, possibly hidden, relationships between selected input variables and the measured output. The resulting neurofuzzy models may be interpreted as sets of linguistic fuzzy rules. This work is essentially concerned with fuzzy and neurofuzzy model building as part of a workstation aimed at improving and standardising treatment protocols in the diagnosis and treatment of breast cancer.